Thanks yet again to Kit Baum, new versions of the -punaf- and -punafcc-
packages are now downloadable from SSC. In Stata, use the -ssc- command
to do this, or -adoupdate- if you already have old versions of -punaf-
and -punafcc- (like last week's versions).

The -punaf- and -punafcc- packages are described as below on my website,
and estimate population unattributable and attributable fractions for
cohort/cross-sectional and case-control/survival data, respectively. The
new versions have had their on-line help updated to refer to each other,
to the new package -regpar- for unattributable and attributable risks,
and to the seminal Lane-Nelder paper on scenario comparisons. The
packages -punaf-, -punafcc- and -regpar- therefore form a suite of
packages for scenario comparison after binary, loglinear or Cox
regression models, although they all function if downloaded separately.

Best wishes
Roger
--
Roger B Newson BSc MSc DPhil
Lecturer in Medical Statistics
Respiratory Epidemiology and Public Health Group
National Heart and Lung Institute
Imperial College London
Royal Brompton Campus
Room 33, Emmanuel Kaye Building
1B Manresa Road
London SW3 6LR
UNITED KINGDOM
Tel: +44 (0)20 7352 8121 ext 3381
Fax: +44 (0)20 7351 8322
Email: r.newson@imperial.ac.uk
Web page: http://www.imperial.ac.uk/nhli/r.newson/
Departmental Web page:
http://www1.imperial.ac.uk/medicine/about/divisions/nhli/respiration/popgenetics/reph/
Opinions expressed are those of the author, not of the institution.
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package punaf from http://www.imperial.ac.uk/nhli/r.newson/stata12
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TITLE
punaf: Population attributable fractions for cohort studies
DESCRIPTION/AUTHOR(S)
punaf calculates confidence intervals for population attributable
fractions, and also for scenario means and their ratio, known as
the population unattributable fraction. punaf can be used after
an estimation command whose predicted values are interpreted as
conditional arithmetic means, such as logit, logistic, poisson,
or glm. It estimates the logs of two scenario means, the
baseline scenario ("Scenario 0") and a fantasy scenario
("Scenario 1"), in which one or more exposure variables are
assumed to be set to particular values (typically zero), and any
other predictor variables in the model are assumed to remain the
same. It also estimates the log of the ratio of the Scenario 1
mean to the Scenario 0 mean. This ratio is known as the
population unattributable fraction, and is subtracted from 1 to
derive the population attributable fraction, defined as the
proportion of the mean of the outcome variable attributable to
living in Scenario 0 instead of Scenario 1.
Author: Roger Newson
Distribution-Date: 01november2011
Stata-Version: 12
INSTALLATION FILES (click here to install)
punaf.ado
punaf_p.ado
punaf.sthlp
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(click here to return to the previous screen)
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package punafcc from http://www.imperial.ac.uk/nhli/r.newson/stata12
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TITLE

punafcc: Population attributable fractions for case-control and
survival studies

DESCRIPTION/AUTHOR(S)
punafcc calculates confidence intervals for population attributable
and unattributable fractions in case-control or survival studies.
punafcc can be used after an estimation command whose parameters are
interpreted as log rate ratios, such as logit or logistic for
case-control data, or stcox for survival data. It estimates the log
of the mean rate ratio, in cases or deaths, between 2 scenarios, a
baseline scenario ("Scenario 0") and a fantasy scenario ("Scenario
1"), in which one or more exposure variables are assumed to be set
to particular values (typically zero), and any other predictor
variables in the model are assumed to remain the same. This ratio
is known as the population unattributable fraction (PUF), and is
subtracted from 1 to derive the population attributable fraction
(PAF), defined as the proportion of the cases or deaths attributable
to living in Scenario 0 instead of Scenario 1.
Author: Roger Newson
Distribution-Date: 01november2011
Stata-Version: 12
INSTALLATION FILES (click here to install)
punafcc.ado
punafcc_p.ado
punafcc.sthlp
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(click here to return to the previous screen)
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